Current 2D and 3D deconvolution and analysis techniques are not well suited for deep tissue microscopy. Most assume the point spread functions (PSFs) to be symmetric or spatially invariant, which is not the case for thick samples. In addition, many utilize prior probability distributions that are not good models of the data that bias the enhancement results. Finally, many techniques are computationally intensive. The long term goal is to develop automatic and unbiased 3D enhancement (deconvolution), segmentation, and quantification tools suitable for deep-tissue multi-photon microscopy, and to implement these tools into multiplatform image analysis software for efficient, interactive quantitation of multi-photon image data. The objective of the current application is to develop efficient 2D enhancement and segmentation techniques that can be used in the analysis of multi-photon data. The approach will be based on (1) estimating the unknown probability distribution of the true but unknown images (i.e. the prior distribution) and (2) using the estimated prior distribution to enhance images prior to segmentation, rather than segmenting them directly. The estimate of the unknown probability distribution will be derived from the acquired data by assuming a Poisson model for the noise and by imposing some physical constraints that are characteristic of deep tissue imaging, such as low photon count. The rationale for the proposed research is that inaccurate assumptions about PSFs and the prior distributions lessen the quality of enhancement and consequently decrease the accuracy and sensitivity of segmentation techniques. The objective will be achieved through the following specific aims: (1) Estimating the prior distributions and using the estimated priors to enhance images via maximum a posteriori (MAP) estimation. The enhanced images will then be segmented via techniques that track object boundaries. (2) Utilizing mathematical morphology to segment objects of known shape from the enhanced images. This approach is innovative because it derives the unknown data probability distributions directly from the data by imposing constraints that are better suited for deep tissue imaging. This will result in the development of unique image analysis tools better suited to the unusual characteristics of deep-tissue fluorescence images. The proposed research is significant, because it will significantly enhance the quantitative capabilities of multi-photon microscopy. Public Health Relevance Statement: The proposed studies will address an under investigated area of deep tissue microscopy. The proposed research will enhance the ability to quantitatively analyze large microscopy image volumes, providing the final link in the effective implementation of multi-photon microscopy as a quantitative method in biomedical research.